Modeling mobile cellular networks based on social characteristics

Ma, J; Ni, W; Yin, J; Liu, RP; Yuan, Y; Fang, B

Modeling mobile cellular networks based on social characteristics

Ma, J Ni, W

Yin, J Liu, RP Yuan, Y Fang, B

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Publisher:: CCC PUBL-AGORA UNIV
Publication Type:: Journal Article
Citation:: International Journal of Computers, Communications and Control, 2016, 11, (4), pp. 480-492
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Ma, J
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Yin, J
dc.contributor.author	Liu, RP
dc.contributor.author	Yuan, Y
dc.contributor.author	Fang, B
dc.date.accessioned	2022-02-08T07:07:10Z
dc.date.available	2022-02-08T07:07:10Z
dc.date.issued	2016-01-01
dc.identifier.citation	International Journal of Computers, Communications and Control, 2016, 11, (4), pp. 480-492
dc.identifier.issn	1841-9836
dc.identifier.issn	1841-9844
dc.identifier.uri	http://hdl.handle.net/10453/154303
dc.description.abstract	Social characteristics have become an important aspect of cellular systems, particularly in next generation networks where cells are miniaturised and social effects can have considerable impacts on network operations. Traffic load demonstrates strong spatial and temporal fluctuations caused by users social activities. In this article, we introduce a new modelling method which integrates the social aspects of individual cells in modelling cellular networks. In the new method, entropy based social characteristics and time sequences of traffic fluctuations are defined as key measures, and jointly evaluated. Spectral clustering techniques can be extended and applied to categorise cells based on these key parameters. Based on the social characteristics respectively, we implement multi-dimensional clustering technologies, and categorize the base stations. Experimental studies are carried out to validate our proposed model, and the effectiveness of the model is confirmed through the consistency between measurements and model. In practice, our modelling method can be used for network planning and parameter dimensioning to facilitate cellular network design, deployments and operations.
dc.language	English
dc.publisher	CCC PUBL-AGORA UNIV
dc.relation.ispartof	International Journal of Computers, Communications and Control
dc.relation.isbasedon	10.15837/ijccc.2016.4.2054
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Modeling mobile cellular networks based on social characteristics
dc.type	Journal Article
utslib.citation.volume	11
utslib.for	0805 Distributed Computing
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2022-02-08T07:07:09Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	4

Abstract:

Social characteristics have become an important aspect of cellular systems, particularly in next generation networks where cells are miniaturised and social effects can have considerable impacts on network operations. Traffic load demonstrates strong spatial and temporal fluctuations caused by users social activities. In this article, we introduce a new modelling method which integrates the social aspects of individual cells in modelling cellular networks. In the new method, entropy based social characteristics and time sequences of traffic fluctuations are defined as key measures, and jointly evaluated. Spectral clustering techniques can be extended and applied to categorise cells based on these key parameters. Based on the social characteristics respectively, we implement multi-dimensional clustering technologies, and categorize the base stations. Experimental studies are carried out to validate our proposed model, and the effectiveness of the model is confirmed through the consistency between measurements and model. In practice, our modelling method can be used for network planning and parameter dimensioning to facilitate cellular network design, deployments and operations.

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http://hdl.handle.net/10453/154303